diff --git a/scripts/tokenizer.py b/scripts/tokenizer.py
index 02963b3..b37a6ee 100644
--- a/scripts/tokenizer.py
+++ b/scripts/tokenizer.py
@@ -7,27 +7,62 @@ import gradio as gr
 
 
 css = """
+@media (prefers-color-scheme: dark) {
 .tokenizer-token{
     cursor: pointer;
 }
-.tokenizer-token-0 {background: rgba(255, 0, 0, 0.05);}
-.tokenizer-token-0:hover {background: rgba(255, 0, 0, 0.15);}
-.tokenizer-token-1 {background: rgba(0, 255, 0, 0.05);}
-.tokenizer-token-1:hover {background: rgba(0, 255, 0, 0.15);}
-.tokenizer-token-2 {background: rgba(0, 0, 255, 0.05);}
-.tokenizer-token-2:hover {background: rgba(0, 0, 255, 0.15);}
-.tokenizer-token-3 {background: rgba(255, 156, 0, 0.05);}
-.tokenizer-token-3:hover {background: rgba(255, 156, 0, 0.15);}
+.tokenizer-token-0 {background: rgba(255, 0, 0, 0.2);}
+.tokenizer-token-0:hover {background: rgba(255, 0, 0, 0.4);}
+.tokenizer-token-1 {background: rgba(0, 255, 0, 0.2);}
+.tokenizer-token-1:hover {background: rgba(0, 255, 0, 0.4);}
+.tokenizer-token-2 {background: rgba(0, 0, 255, 0.2);}
+.tokenizer-token-2:hover {background: rgba(0, 0, 255, 0.4);}
+.tokenizer-token-3 {background: rgba(255, 156, 0, 0.2);}
+.tokenizer-token-3:hover {background: rgba(255, 156, 0, 0.4);}
+}
+@media (prefers-color-scheme: light) {
+.tokenizer-token{
+    cursor: pointer;
+}
+.tokenizer-token-0 {background: rgba(255, 0, 0, 0.1);}
+.tokenizer-token-0:hover {background: rgba(255, 0, 0, 0.2);}
+.tokenizer-token-1 {background: rgba(0, 255, 0, 0.1);}
+.tokenizer-token-1:hover {background: rgba(0, 255, 0, 0.2);}
+.tokenizer-token-2 {background: rgba(0, 0, 255, 0.1);}
+.tokenizer-token-2:hover {background: rgba(0, 0, 255, 0.2);}
+.tokenizer-token-3 {background: rgba(255, 156, 0, 0.1);}
+.tokenizer-token-3:hover {background: rgba(255, 156, 0, 0.2);}
+}
 """
 
 
-def tokenize(text, input_is_ids=False):
+def tokenize(text, current_step=1, total_step=1, AND_block=0, simple_input=False, input_is_ids=False):
     clip: FrozenCLIPEmbedder = shared.sd_model.cond_stage_model.wrapped
 
+    token_count = None
     if input_is_ids:
         tokens = [int(x.strip()) for x in text.split(",")]
-    else:
+    elif simple_input:
         tokens = clip.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
+    else:
+        from modules import sd_hijack, prompt_parser
+        from functools import reduce
+        _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+        prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, int(total_step))
+        flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+        prompts = [prompt_text for step, prompt_text in flat_prompts]
+
+        def find_current_prompt_idx(c_step, a_block):
+            _idx = 0
+            for i, prompts_block in enumerate(prompt_schedules):
+                for step_prompt_chunk in prompts_block:
+                    if i == a_block:
+                        if c_step <= step_prompt_chunk[0]:
+                            return _idx
+                    _idx += 1
+
+        idx = find_current_prompt_idx(current_step, AND_block)
+        tokens, token_count, max_length = [sd_hijack.model_hijack.tokenize(prompt) for prompt in prompts][idx]
 
     vocab = {v: k for k, v in clip.tokenizer.get_vocab().items()}
 
@@ -44,7 +79,10 @@ def tokenize(text, input_is_ids=False):
 
         def wordscode(ids, word):
             nonlocal class_index
-            res = f"""<span class='tokenizer-token tokenizer-token-{class_index%4}' title='{html.escape(", ".join([str(x) for x in ids]))}'>{html.escape(word)}</span>"""
+            if ids != [clip.tokenizer.eos_token_id]:
+                res = f"""<span class='tokenizer-token tokenizer-token-{class_index%4}' title='{html.escape(", ".join([str(x) for x in ids]))}'>{html.escape(word)}</span>"""
+            else:
+                res = f"""<span class='tokenizer-token tokenizer-token-4' title='{html.escape(", ".join([str(x) for x in ids]))}'>{html.escape(word)}</span>"""
             class_index += 1
             return res
 
@@ -83,9 +121,11 @@ def tokenize(text, input_is_ids=False):
 
     dump(last=True)
 
+    if token_count is None:
+        token_count = len(ids)
     ids_html = f"""
 <p>
-Token count: {len(ids)}<br>
+Token count: {token_count}/{len(ids)}<br>
 {", ".join([str(x) for x in ids])}
 </p>
 """
@@ -98,7 +138,9 @@ def add_tab():
         gr.HTML(f"""
 <style>{css}</style>
 <p>
-Before your text is sent to the neural network, it gets turned into numbers in a process called tokenization. These tokens are how the neural network reads and interprets text. Thanks to our great friends at Shousetsu愛 for inspiration for this feature.
+Before your text is sent to the neural network, it gets turned into numbers in a process called tokenization. These tokens are how the neural network reads and interprets text. Thanks to our great friends at Shousetsu愛 for inspiration for this feature.<br>
+Depending on your setting, text will be first parsed by webui to calculate prompt attention like (text) and [text], and scheduler like [a:b:0.5], and the capital AND like a AND b before tokenization. This extension processes your text like this as well.<br>
+To disable this feature, check on "Don't parse webui special grammar".
 </p>
 """)
 
@@ -106,6 +148,11 @@ Before your text is sent to the neural network, it gets turned into numbers in a
             with gr.Tab("Text input", id="input_text"):
                 prompt = gr.Textbox(label="Prompt", elem_id="tokenizer_prompt", show_label=False, lines=8, placeholder="Prompt for tokenization")
                 go = gr.Button(value="Tokenize", variant="primary")
+                is_simple = gr.Checkbox(label="Don't parse webui special grammar", interactive=True)
+                with gr.Row():
+                    current_step = gr.Number(label='Current sampling steps', value=1, step=1, interactive=True)
+                    total_step = gr.Number(label='Total sampling steps', value=28, step=1, interactive=True)
+                    and_block = gr.Number(label='Which block of prompts (separated by AND) to tokenize', value=0, step=1, interactive=True)
 
             with gr.Tab("ID input", id="input_ids"):
                 prompt_ids = gr.Textbox(label="Prompt", elem_id="tokenizer_prompt", show_label=False, lines=8, placeholder="Ids for tokenization (example: 9061, 631, 736)")
@@ -120,7 +167,7 @@ Before your text is sent to the neural network, it gets turned into numbers in a
 
         go.click(
             fn=tokenize,
-            inputs=[prompt],
+            inputs=[prompt, current_step, total_step, and_block, is_simple],
             outputs=[tokenized_text, tokens],
         )